Literature DB >> 3411254

The cue interaction model of depth perception: a stability analysis.

R Chipalkatti1, M A Arbib.   

Abstract

In this paper, we offer a stability analysis of "the cue interaction model" of depth perception (House (1984]. Depth estimation using stereopsis suffers from the "matching problem", the problem of correctly matching the retinal image of a feature in one eye, to its retinal image in the other eye. The Cue Interaction Model overcomes this by using monocular cues to disambiguate between the "correct matches" and the "incorrect matches". Its decision making is based on the concept of cooperation and competition in a neural network. A general class of cooperative and competitive models has been mathematically analysed by Amari and Arbib (1977), with special attention given to equilibrium states and stability. In this paper we adapt their methods to study the above model. In particular, we prove that if the parameters are correctly tuned, the model successfully achieves its goals by suppressing the cues which represent the "incorrect matches".

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Year:  1988        PMID: 3411254     DOI: 10.1007/bf00277391

Source DB:  PubMed          Journal:  J Math Biol        ISSN: 0303-6812            Impact factor:   2.259


  4 in total

1.  Cooperative computation of stereo disparity.

Authors:  D Marr; T Poggio
Journal:  Science       Date:  1976-10-15       Impact factor: 47.728

Review 2.  The analysis of stereopsis.

Authors:  G F Poggio; T Poggio
Journal:  Annu Rev Neurosci       Date:  1984       Impact factor: 12.449

3.  Stereopsis in toads.

Authors:  T Collett
Journal:  Nature       Date:  1977-05-26       Impact factor: 49.962

4.  An inference upon the neural network finding binocular correspondence.

Authors:  Y Hirai; K Fukushima
Journal:  Biol Cybern       Date:  1978-12-15       Impact factor: 2.086

  4 in total
  2 in total

1.  The prey localisation model: a stability analysis.

Authors:  R Chipalkatti; M A Arbib
Journal:  Biol Cybern       Date:  1987       Impact factor: 2.086

2.  Saccadic motor planning by integrating visual information and pre-information on neural dynamic fields.

Authors:  K Kopecz; G Schöner
Journal:  Biol Cybern       Date:  1995-06       Impact factor: 2.086

  2 in total

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